Data handling in data fusion: Methodologies and applications

Autores
Azcarate, Silvana Mariela; Ríos Reina, Rocío; Amigo, José M.; Goicoechea, Hector Casimiro
Año de publicación
2021
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
The use of data fusion methodologies has increased at the same rhythm as the capability of modern analytical laboratories of measuring sample from multiple sources. Almost all data fusion strategies can be grouped into three levels, they fuse the data differently with the sole aim of obtaining a better response (qualitative or quantitative) than that obtained by the instruments individually. One of the major key points for the data fusion methodologies to succeed is the understanding of the data structure obtained from a particular instrument. This point is not exhaustively commented in the literature focused on data fusion, sometimes paying too much attention to the algorithms instead. This manuscript explains data fusion from the structure of the different data obtained by different analytical platforms. Special attention will be given to the nature of the data and the relationships between the samples and the variables, as well as within the variables.
Fil: Azcarate, Silvana Mariela. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Ciencias de la Tierra y Ambientales de La Pampa. Universidad Nacional de La Pampa. Facultad de Ciencias Exactas y Naturales. Instituto de Ciencias de la Tierra y Ambientales de La Pampa; Argentina
Fil: Ríos Reina, Rocío. Universidad Pablo de Olavide.; España
Fil: Amigo, José M.. Universidad del País Vasco; España
Fil: Goicoechea, Hector Casimiro. Universidad Nacional del Litoral; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe; Argentina
Materia
DATA FUSION STRATEGIES
DATA STRUCTURE
HIGH-LEVEL
LOW-LEVEL
MID-LEVEL
MULTILEVEL
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/165095
Acceder
id CONICETDig_c7353bd43db35a9281367c17c58e1f75
oai_identifier_str oai:ri.conicet.gov.ar:11336/165095
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Data handling in data fusion: Methodologies and applicationsAzcarate, Silvana MarielaRíos Reina, RocíoAmigo, José M.Goicoechea, Hector CasimiroDATA FUSION STRATEGIESDATA STRUCTUREHIGH-LEVELLOW-LEVELMID-LEVELMULTILEVELhttps://purl.org/becyt/ford/1.4https://purl.org/becyt/ford/1The use of data fusion methodologies has increased at the same rhythm as the capability of modern analytical laboratories of measuring sample from multiple sources. Almost all data fusion strategies can be grouped into three levels, they fuse the data differently with the sole aim of obtaining a better response (qualitative or quantitative) than that obtained by the instruments individually. One of the major key points for the data fusion methodologies to succeed is the understanding of the data structure obtained from a particular instrument. This point is not exhaustively commented in the literature focused on data fusion, sometimes paying too much attention to the algorithms instead. This manuscript explains data fusion from the structure of the different data obtained by different analytical platforms. Special attention will be given to the nature of the data and the relationships between the samples and the variables, as well as within the variables.Fil: Azcarate, Silvana Mariela. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Ciencias de la Tierra y Ambientales de La Pampa. Universidad Nacional de La Pampa. Facultad de Ciencias Exactas y Naturales. Instituto de Ciencias de la Tierra y Ambientales de La Pampa; ArgentinaFil: Ríos Reina, Rocío. Universidad Pablo de Olavide.; EspañaFil: Amigo, José M.. Universidad del País Vasco; EspañaFil: Goicoechea, Hector Casimiro. Universidad Nacional del Litoral; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe; ArgentinaElsevier2021-10info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/165095Azcarate, Silvana Mariela; Ríos Reina, Rocío; Amigo, José M.; Goicoechea, Hector Casimiro; Data handling in data fusion: Methodologies and applications; Elsevier; Trac-Trends In Analytical Chemistry; 143; 10-2021; 1-500165-9936CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://linkinghub.elsevier.com/retrieve/pii/S0165993621001783info:eu-repo/semantics/altIdentifier/doi/10.1016/j.trac.2021.116355info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-nd/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T09:55:21Zoai:ri.conicet.gov.ar:11336/165095instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-29 09:55:22.197CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Data handling in data fusion: Methodologies and applications
title Data handling in data fusion: Methodologies and applications
spellingShingle Data handling in data fusion: Methodologies and applications
Azcarate, Silvana Mariela
DATA FUSION STRATEGIES
DATA STRUCTURE
HIGH-LEVEL
LOW-LEVEL
MID-LEVEL
MULTILEVEL
title_short Data handling in data fusion: Methodologies and applications
title_full Data handling in data fusion: Methodologies and applications
title_fullStr Data handling in data fusion: Methodologies and applications
title_full_unstemmed Data handling in data fusion: Methodologies and applications
title_sort Data handling in data fusion: Methodologies and applications
dc.creator.none.fl_str_mv Azcarate, Silvana Mariela
Ríos Reina, Rocío
Amigo, José M.
Goicoechea, Hector Casimiro
author Azcarate, Silvana Mariela
author_facet Azcarate, Silvana Mariela
Ríos Reina, Rocío
Amigo, José M.
Goicoechea, Hector Casimiro
author_role author
author2 Ríos Reina, Rocío
Amigo, José M.
Goicoechea, Hector Casimiro
author2_role author
author
author
dc.subject.none.fl_str_mv DATA FUSION STRATEGIES
DATA STRUCTURE
HIGH-LEVEL
LOW-LEVEL
MID-LEVEL
MULTILEVEL
topic DATA FUSION STRATEGIES
DATA STRUCTURE
HIGH-LEVEL
LOW-LEVEL
MID-LEVEL
MULTILEVEL
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.4
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv The use of data fusion methodologies has increased at the same rhythm as the capability of modern analytical laboratories of measuring sample from multiple sources. Almost all data fusion strategies can be grouped into three levels, they fuse the data differently with the sole aim of obtaining a better response (qualitative or quantitative) than that obtained by the instruments individually. One of the major key points for the data fusion methodologies to succeed is the understanding of the data structure obtained from a particular instrument. This point is not exhaustively commented in the literature focused on data fusion, sometimes paying too much attention to the algorithms instead. This manuscript explains data fusion from the structure of the different data obtained by different analytical platforms. Special attention will be given to the nature of the data and the relationships between the samples and the variables, as well as within the variables.
Fil: Azcarate, Silvana Mariela. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Ciencias de la Tierra y Ambientales de La Pampa. Universidad Nacional de La Pampa. Facultad de Ciencias Exactas y Naturales. Instituto de Ciencias de la Tierra y Ambientales de La Pampa; Argentina
Fil: Ríos Reina, Rocío. Universidad Pablo de Olavide.; España
Fil: Amigo, José M.. Universidad del País Vasco; España
Fil: Goicoechea, Hector Casimiro. Universidad Nacional del Litoral; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe; Argentina
description The use of data fusion methodologies has increased at the same rhythm as the capability of modern analytical laboratories of measuring sample from multiple sources. Almost all data fusion strategies can be grouped into three levels, they fuse the data differently with the sole aim of obtaining a better response (qualitative or quantitative) than that obtained by the instruments individually. One of the major key points for the data fusion methodologies to succeed is the understanding of the data structure obtained from a particular instrument. This point is not exhaustively commented in the literature focused on data fusion, sometimes paying too much attention to the algorithms instead. This manuscript explains data fusion from the structure of the different data obtained by different analytical platforms. Special attention will be given to the nature of the data and the relationships between the samples and the variables, as well as within the variables.
publishDate 2021
dc.date.none.fl_str_mv 2021-10
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/165095
Azcarate, Silvana Mariela; Ríos Reina, Rocío; Amigo, José M.; Goicoechea, Hector Casimiro; Data handling in data fusion: Methodologies and applications; Elsevier; Trac-Trends In Analytical Chemistry; 143; 10-2021; 1-50
0165-9936
CONICET Digital
CONICET
url http://hdl.handle.net/11336/165095
identifier_str_mv Azcarate, Silvana Mariela; Ríos Reina, Rocío; Amigo, José M.; Goicoechea, Hector Casimiro; Data handling in data fusion: Methodologies and applications; Elsevier; Trac-Trends In Analytical Chemistry; 143; 10-2021; 1-50
0165-9936
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://linkinghub.elsevier.com/retrieve/pii/S0165993621001783
info:eu-repo/semantics/altIdentifier/doi/10.1016/j.trac.2021.116355
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
_version_ 1844613669598527488
score 13.070432

Publicaciones similares